Publications

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The objective of the experimental effort is to provide a model particle system that will enable modeling of the macroscopic rheology from the interfacial and environmental structure of the particles and solvent or melt as functions of applied shear and volume fraction of the solid particles. This chapter describes the choice of the model particle system, methods for synthesis and characterization, and results from characterization of colloidal dispersion, particle film formation, and the shear and oscillatory rheology in the system. Surface characterization of the grafted PDMS interface, dispersion characterization of the colloids, and rheological characterization of the dispersions as a function of volume fraction were conducted.

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Nanoparticle interactions and their impact on particle dispersion and rheology are well known to be functions of the interfacial structure between the particle and the fluid phase. The dispersion and flow properties of a titania nanopowder were evaluated in polydimethylsiloxane fluid using ''grafted to'' surface modification of the titania with short molecular weight PDMS polymers. The interaction energy between particles was modeled using analytical expressions as well as dynamic functional theory for polymer surface chains. Particle dynamics as a function of volume fraction were characterized using light scattering, acoustic spectroscopy, and shear and oscillatory measurements. Autophobic dewetting is a novel short range interaction in this system that may be impacting the maximum packing fraction of particles in a suspension.

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We use the recent fluids density functional theory of Tripathi and Chapman [Phys. Rev. Lett. 94, 087801 (2005); J. Chem. Phys. 122, 094506 (2005)] to investigate the phase behavior of athermal polymer/nanoparticle blends near a substrate. The blends are modeled as a mixture of hard spheres and freely jointed hard chains, near a hard wall. There is a first order phase transition present in these blends in which the nanoparticles expel the polymer from the surface to form a monolayer at a certain nanoparticle concentration. The nanoparticle transition density depends on the length of the polymer, the nanoparticle diameter, and the overall bulk density of the system. The phase transition is due to both packing entropy effects related to size asymmetry between the components and to the polymer configurational entropy, justifying the so-called "entropic push" observed in experiments. In addition, a layered state is found at higher densities which resembles that in colloidal crystals, in which the polymer and nanoparticles form alternating discrete layers. We show that this laminar state has nearly the same free energy as the homogeneously mixed fluid in the bulk and is nucleated by the surface. © 2008 American Institute of Physics.

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A new formulation of configurational-bias Monte Carlo that uses arbitrary distributions to generate trial bond lengths, angles and dihedrals is described and shown to provide similar acceptance rates with substantially less computational effort. Several different trial distributions are studied and a linear combination of the ideal distribution plus Gaussian distributions automatically fit to the energetic and ideal terms is found to give the best results. The use of these arbitrary trial distributions enables a new formulation of coupled-decoupled configurational bias Monte Carlo that has significantly higher acceptance rates for cyclic molecules. The chemical potential measured via a modified Widom insertion is found to be ill-defined in the case of a molecule that has flexible bond lengths due to the unbounded probability distribution that describes the distance between any two atoms. We propose a simple standard state that allows the computation of consistent chemical potentials for molecules with flexible bonds. We show that the chemical potential via Widom insertion is not computed properly for molecules with Coulombic interactions when the number of trials for any of the nonbonded selection steps is greater than one. Finally, we demonstrate the power of the new algorithms by sampling the side-chain conformations of a polypeptide.

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Carbon nanotubes (CNT) are unique nanoscale building blocks for a variety of materials and applications, from nanocomposites, sensors and molecular electronics to drug and vaccine delivery. An important step towards realizing these applications is the ability to controllably self-assemble the nanotubes into larger structures. Recently, amphiphilic peptide helices have been shown to bind to carbon nanotubes and thus solubilize them in water. Furthermore, the peptides then facilitate the assembly of the peptide-wrapped nanotubes into supramolecular, well-aligned fibers. We investigate the role that molecular modeling can play in elucidating the interactions between the peptides and the carbon nanotubes in aqueous solution. Using ab initio methods, we have studied the interactions between water and CNTs. Classical simulations can be used on larger length scales. However, it is difficult to sample in atomistic detail large biomolecules such as the amphiphilic peptide of interest here. Thus, we have explored both new sampling methods using configurational-bias Monte Carlo simulations, and also coarse-grained models for peptides described in the literature. An improved capability to model these inorganichiopolymer interfaces could be used to generate improved understanding of peptide-nanotube self-assembly, eventually leading to the engineering of new peptides for specific self-assembly goals.

Understanding the properties and behavior of biomembranes is fundamental to many biological processes and technologies. Microdomains in biomembranes or ''lipid rafts'' are now known to be an integral part of cell signaling, vesicle formation, fusion processes, protein trafficking, and viral and toxin infection processes. Understanding how microdomains form, how they depend on membrane constituents, and how they act not only has biological implications, but also will impact Sandia's effort in development of membranes that structurally adapt to their environment in a controlled manner. To provide such understanding, we created physically-based models of biomembranes. Molecular dynamics (MD) simulations and classical density functional theory (DFT) calculations using these models were applied to phenomena such as microdomain formation, membrane fusion, pattern formation, and protein insertion. Because lipid dynamics and self-organization in membranes occur on length and time scales beyond atomistic MD, we used coarse-grained models of double tail lipid molecules that spontaneously self-assemble into bilayers. DFT provided equilibrium information on membrane structure. Experimental work was performed to further help elucidate the fundamental membrane organization principles.

Polymer reference interaction site model (PRISM) calculations and molecular dynamics (MD) simulations were carried out on poly(ethylene oxide) liquids using a force field of Smith, Jaffe, and Yoon. The intermolecular pair correlation functions and radius of gyration from theory were in very good agreement with MD simulations when the partial charges were turned off. When the charges were turned on, considerably more structure was seen in the intermolecular correlations obtained from MD simulation. Moreover, the radius of gyration increased by 38% due to electrostatic repulsions along the chain backbone. Because the partial charges greatly affect the structure, significant differences were seen between the PRISM calculations (without charges) and the wide angle neutron scattering measurements of Annis and coworkers for the total structure factor, and the hydrogen/hydrogen intermolecular correlation function. This is in contrast to previous PRISM calculations on poly (dimethyl siloxane). © 2005 Elsevier Ltd. All rights reserved.

Classical density functional theory (DFT) is applied to study properties of fully detailed, realistic models of poly(dimethylsiloxane) liquids near silica surfaces and compared to results from molecular dynamics simulations. In solving the DFT equations, the direct correlation functions are obtained from the polymer reference interaction site model (PRISM) theory for the repulsive parts of the interatomic interactions, and the attractions are treated via the random-phase approximation (RPA). Good agreement between density profiles calculated from DFT and from the simulations is obtained with empirical scaling of the direct correlation functions. Separate scaling factors are required for the PRISM and RPA parts of the direct correlation functions. Theoretical predictions of stress profiles, normal pressure, and surface tensions are also in reasonable agreement with simulation results.